On Average, corporate teams order food for their employees once every two weeks. Large scale food ordering for such corporate teams is very chaotic and does not take into account everyone’s opinion. No easy way exists to decide which restaurant to order from, or eat at. The monday.com platform is used by more than 100,000 such teams. We wanted to create an efficient solution for this issue and integrate it with the monday.com platform.
What it does
LunchDay is a monday.com app that enables corporate teams to decide which restaurant to eat at, or order from, based on our novel voting algorithm. The team manager first starts a poll, specifying the budget per person, and the time limit of the poll. All the employees in the team are notified of this poll. Our app then gives cuisine suggestions to each employee, which they can vote on. This poll can be tracked live by the manager. After the poll ends. Our algorithm compiles this data and provides restaurant suggestions to the poll manager, based on the most popular cuisine, and the budget. Our interface is super simple and easy to use, and we believe that it can improve the efficiency of the current system by a large magnitude.
How we built it
- React for the front-end application.
- Monday Apps Platform.
- Python Flask for back-end testing.
- Monday.com API.
- Azure serverless functions for endpoints.
- Google Places API for restaurant recommendations.
- Ngrok for testing.
- Figma for UI design.
Challenges we ran into
- We were pressed for time given that we took time to settle on a final idea.
- Integrating the React with our back-end architecture and endpoints.
- Selecting the optimal tech architecture that struck the perfect balance between performance and development time.
Accomplishments that we're proud of
- Creating a fully functional monday.com application when pressed for time.
- Designing and implementing a clean and professional user interface in the form of a monday.com app.
- Successfully integrating our React front-end and Python and Azure serverless back-end.
- Successfully integrating our voting mechanism and restaurant recommendation system into our front-end.
What we learned
- How to use the Monday Apps platform to create an application in React.
- How to incorporate the Monday.com API into our project.
- Further improving our skills at React.
- Serverless endpoint architecture using Python and Azure.
What's next for LunchDay
- Publishing MVP on Monday Apps Marketplace.
- Integrating with Yelp APIs for restaurant reviews and reservations.
- Integrating with grubhub and doordash to foster a self sufficient food-ordering ecosystem.
- Additional features for employees to vote on specific dishes, and to make our voting system more accurate.